Citations

Abstract Background: Deletions in MTAP occur in 10-15% of cancers and are associated with poor prognosis. MTAP deletion (MTAPdel) leads to accumulation of MTA which causes partial inhibition of PRMT5, creating a synthetic lethality approach with PRMT5 inhibitors. Multiple investigational MTA-cooperative PRMT5 inhibitors have demonstrated early clinical efficacy in MTAPdel NSCLC and other cancers. MTAPdel, PRMT5 expression, and clinical outcomes to standard of care immune checkpoint inhibitors (ICI) and chemotherapy (chemo) have not been well characterized. Methods: The Tempus Lens Platform (Tempus AI, Inc., Chicago, IL) was used to query the Tempus multimodal de-identified database and establish and subsequently analyze a cohort of patients (pts)with NSCLC and DNA (Tempus xT) and RNA (Tempus xR) testing, treated with front-line (1L) ICI with or without chemo (N=3,676). PRMT5 expression was normalized as transcripts per million (TPM) and reported as log2(TPM + 1). Pts were classified into high vs low groups based on median PRMT5 expression and into MTAPdel vs intact (MTAPwt) based on MTAP biallelic loss. Immune infiltration was estimated using Quantiseq. Real-world overall survival (rwOS) was defined as the time from 1L start to death or last known follow-up. Real-world progression-free survival (rwPFS) was defined as the time from 1L start to death, progression or last known follow-up. Hazard ratios (HR) were calculated using Cox proportional hazards models and adjusted for age, sex, histology, smoking status and PDL1 status. Results: PRMT5 expression was moderately increased in pts with MTAPdel (6.65 v 6.70, p=0.012). Alterations in MYC, SMARCA4 and KEAP1 were enriched in PRMT5-high tumors in both MTAPdel and MTAPwt pts (p<0.03). Pts with PRMT5-high tumors had decreased infiltrate of multiple immune cells, including B cells, macrophages (M1 and M2), NK cells, CD4 and CD8 T cells (p<0.001). Pts with MTAPdel/PRMT5-high expression had shorter rwOS compared to MTAPwt/PRMT5-low when treated with ICI alone (HR 1.58, 95% CI 1.11-2.24, p=0.011) or with ICI + chemo (HR 1.65, 95% CI 1.34-2.03, p<0.001). These associations persisted on multivariate analysis. Pts with MTAPdel/PRMT5-high expression were also associated with worse rwPFS when treated with ICI + chemo (HR 1.5, 95% CI 1.22-1.86, p<0.001) on univariate and multivariate analysis, but did not associate with rwPFS in pts treated with ICI alone (HR=0.97, 95% CI 0.66-1.42, p=0.9). Conclusion: PRMT5 expression defines an immune suppressed subpopulation of pts with NSCLC. Pts with elevated PRMT5 expression and MTAPdel have poor outcomes with ICI+/- chemo, indicating PRMT5 RNA expression levels can be used to inform clinical trial designs, particularly those that are testing novel combinatorial strategies. Citation Format: Natalie Vokes, Stamatina Fragkogianni, Pooja A. Shah, Santiago G. Trevino, Konstantinos Leventakos, Lei Deng, Matthew Lee, Tali Azenkot, KAYLA LAYNG, JACOB MERCER, Jyoti Patel, Jordi Rodon Ahnert. High PRMT5 expression is associated with decreased immune infiltrate and worse outcomes to immune checkpoint inhibitors in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2446.

Abstract Background: The increasing volume and diversity of clinical genomic reports pose a significant challenge across healthcare institutions for the interpretation and proper implementation of precision oncology research. Reports from multiple different vendors (e.g., Invitae, Ambry Genetics, Foundation Medicine) are typically PDFs and exhibit substantial heterogeneity in panel design, gene coverage, and reporting standards, which hinders efficient retrospective data mining and patient cohort identification within the electronic medical records. We sought to develop a framework to interrogate all of these reports. Methods: We extracted genomic reports from patients with breast cancer treated with neoadjuvant chemotherapy and developed MolHarmonizer, a novel, scalable framework leveraging Python and Gemini LLMs, designed to process and harmonize genomic data from disparate multi-vendor reports. Gemini LLMs are employed explicitly for robust information extraction, normalization, and structuring of key genomic features, transforming unstructured data into a unified, queryable dataset. Results: Our MolHarmonizer framework successfully processed 1,147 genomic reports from 1703 breast cancer patients (2006-2023) from 23 different companies, demonstrating robust capability to extract and standardize critical actionable biomarkers. Data sources included Invitae (n=554), Ambry Genetics (n=189), Natera (n=95), Mayo Clinic (n=88), Tempus (n=63), Guardant Health (n=47), and Foundation Medicine (n=37), with others contributing less than 20 reports. Of the samples, 827/1147 (72.1%) were germline (blood/saliva). A majority of the patients were tested using the panels due to a personal/family history (n=925). Overall, 413/1147 (36.0%) reports identified at least one mutation. For breast cancer, 75 reports showed BRCA1/2 mutations (37 BRCA1, 37 BRCA2, and one patient with both BRCA1 and 2). Other mutations identified included: PIK3CA (n=40), TP53 (n=96), PTEN (n=21), ESR1 (n=13) and AKT1/2 (n=8). Conclusion: MolHarmonizer, a powerful framework leveraging Gemini LLMs, effectively addresses genomic data heterogeneity by automating biomarker extraction and harmonization. This enables rapid cohort identification and deep retrospective analyses for clinical insights, biomarker discovery, understanding disease history, facilitating novel pattern discovery, e.g., predicting BRCA1 mutations from WSI, and accelerating research within our neoadjuvant BC cohort. Future plans include expanding to include over 20,000 breast cancer patients, developing a user-friendly chatbot, and ensuring inter-institutional adaptability for a variety of complex diseases. Citation Format: Krishna Rani Kalari, Xiaojia Tang, Thanmayee Boyapati, Tanya L. Hoskin, Sumathilatha Myla, Sumedha G. Penheiter, Richard M. Weinshilboum, Liewei Wang, Hamid R. Tizhoosh, Karthik Vikram Giridhar, Matthew P. Goetz, Judy C. Boughey. Development of an LLM framework for analysis of heterogeneous breast cancer patients genomic reports [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2756.

Abstract Large Language Models (LLM) are being widely adopted into the medical field for their impressive ability to analyze and summarize large amounts of text data. These models enable clinicians and researchers to extract meaningful insights from complex datasets and may assist with decision making. Here we present our workflows and application of LLM for the interpretation and summarization of clinical data related to CAR-T cell therapy. Using an LLM (Gemini 2.5 pro) in the Google Cloud Computing (GCP) environment, two applications were developed to analyze CAR-T cell therapy clinical data: 1) extracting and summarizing CRS and ICANS event-related data to streamline the compliance team workflow, and 2) identifying features available at time of CAR-T infusion able to classify patients into high- or low-monitoring needs 14 days post CAR-T. Patient data (vitals, labs, hematology notes, and EKGs) were extracted from the electronic medical record (EMR) using Google BigQuery into SQL tables in GCP. For each application, relevant data fields were retrieved, formatted into JSON objects, and embedded in the LLM prompt for context-aware processing. Both applications have undergone iterative prompt engineering after analyzing the LLM output against ground truth data in the EMR. For the application extracting and summarizing CRS and ICANS events, the LLM was optimized on Mayo Clinic Rochester data. When compared to the IEC compliance database, the LLM achieved 100% accuracy and F1 score for CRS events, and 96% accuracy and 82% F1 score for ICANS events. The match rate for CRS and ICANS grades were 83% and 89% respectively. We applied the same LLM to Mayo Clinic Arizona (MCA) and Mayo Clinic Florida (MCF) cases, who document clinical notes and toxicity flowsheet differently, and achieved an accuracy (MCA: 93%, MCF:93%) and F1 score (MCA: 96%, MCF: 96%) for CRS and accuracy (MCA: 81%, MCF:82%) and F1 score (MCA: 76%, MCF: 75%) for ICANS. LLM was able to capture events missed by manual reviews. For most of the discrepancies where compliance team final adjudication is needed, LLM will be updated to flag discrepancies for review by the compliance team. For the application related to the monitoring needs 14 days post CAR-T, the LLM identified 5 categories predictive of high or low monitoring needs post CAR-T infusion (disease status, inflammatory and tumor burden markers, hematologic status, renal function and performance status). Our model achieved a sensitivity of 85.7%, specificity of 23.8%, and F1 score of 65.5% for our first cohort, compared with data extracted by the LLM from the EMR. For our second cohort, with demographics statistically similar to cohort 1 and using the same 5 categories, the LLM achieved a sensitivity of 83.3%, a specificity of 28.6%, and F1 score of 65.4%. Our workflow and applications of LLM’s provide examples and guidance to others interested in applying LLM’s for clinical and research applications. Citation Format: Emmanuel Contreras Guzman, Matthew Jankowski, Andre De Menezes Silva Corraes, Malvika Gupta, Monica L. Shaw, Madiha Iqbal, Talal Hilal, Saurabh Chhabra, Ricardo Daniel Parrondo, Jody K. Mclean, Kim R. Riester, Kayla Joseph, Melinda Tan, Holly Ross, Cleyonia Barnett, Sylvia Carter, Semy Girmay, Rachel Wolan, Milana Ramsey, Christian Downhour, Kristy Morgan, Shae Sibley, Erica Rushing, Lucy Holmes, Allison Burgstahler, Stephen M. Ansell, Hassan Alkhateeb, Matthew Hathcock, Ramona Bruno, Allison C. Rosenthal, Hemant Murthy, Patrick B. Johnston, Jonas Paludo, Yi Lin. Applications of large language models to CAR-T cell therapy clinical data using Google cloud computing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2740.

Abstract Introduction: Pathological complete response (pCR) is a strong prognostic marker, but survival outcomes comparing treatment to control do not reliably align with treatment-control differences in pCR rates in breast cancer. A novel Bayesian hierarchical framework models treatments within trials, allowing us to predict treatment effects on distant recurrence-free survival (DRFS) from pCR with greater accuracy. Methods: We analyzed 12 neoadjuvant breast cancer trials (6,000 patients, all HR/HER2 subtypes; including I-SPY2). The framework of Burzykowski, Molenberghs & Buyse (2005) is extended to a novel arm-based hierarchical structure providing a distribution of pCR and DRFS treatment effects controlling for subtype (HR/HER2), N and T stage, grade, and calendar year. Three held-out trials (877 patients; 26 regimens; med follow-up >4 years) validate predictions of DRFS treatment benefit from pCR. All data was used to estimate treatment-effect correlation and the surrogate threshold effect (STE). Analyses were repeated for Residual Cancer Burden Index (binary RCB01, continuous RCB). Results: Predicted probability of DRFS benefit closely matched actual DRFS follow-up (mean absolute error 0.06; Pearson r = 0.9). Across all trials, pCR showed moderate surrogacy (ρ = 0.82, R2 = 0.67). A 60% increase in pCR odds achieves ≥95% probability of DRFS benefit (STE = OR of 1.60). RCB outperforms pCR across all metrics, with 92% sensitivity and 93% specificity for detecting DRFS benefit in 26 validation regimens (Table 1). Conclusion: This novel Bayesian meta-analytic framework reveals that pCR, RCB01 and continuous RCB reliably predict survival benefit across heterogeneous trials, with RCB performing the best in an external validation. This provides a statistical foundation for accelerated approval decisions using robust early biomarkers in modern neoadjuvant trial designs by accurately predicting survival at the treatment arm level. Citation Format: Keli S. Santos-Parker, Jessica R. Santos-Parker, W. Fraser Symmans, Laura J. Esserman, Christina Yau, Angie DeMichele, Laura van't Veer, Doug Yee, Fabien Reyal, Helena Earl, Jean Abraham, David Cameron, Peter Hall, Judy Boughey, Matthew Goetz, Gabe Sonke, Miguel Martín, Sara López-Tarruella, Priyanka Sharma, Rachel Freiberg, Jane Perlmutter, Aditya Bardia, Martin Eklund, Rachel Freiberg, Lajos Pusztai. A novel statistical framework for surrogate endpoint prediction of survival in neoadjuvant breast cancer trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1401.

Abstract Therapeutic resistance limits the efficacy of radiation therapy in many tumors and provides a compelling rationale to develop novel treatments to enhance radiation sensitivity. DNA-dependent protein kinase (DNA-PK) is a major mediator of DNA repair following radiation, and DNA-PK inhibitors are effective radiosensitizers. WNC0901 is a potent DNA-PK inhibitor in both a cell-free kinase assay (IC50 = 0.073 nM) and a cell-based assay (IC50 = 76 nM). WNC0901 has a 30-fold specificity for DNA-PK relative to other PI3K-related kinases. In a broader kinome screen (363 kinases), 1 μM WNC0901 resulted in >25% inhibition of only five other kinases. There is robust inhibition of radiation-induced DNA-PK autophosphorylation (Ser-2056), as analyzed by Western blotting, with maximal effect at 300 nM WNC0901 in A549 (lung carcinoma), U2OS (osteosarcoma), and HT29 (colorectal) cells. The efficacy of WNC0901 as a radiosensitizer was evaluated in clonogenic assays across these same three radioresistant tumor models. Following treatment with graded concentrations of WNC0901 (0-1000 nM) combined with 2.5 Gy radiation, maximal effects were observed at a 300 nM concentration in A549 (88-fold reduction in relative clonogenic survival compared to RT alone, p<0.001), U2OS (5.8-fold decrease, p=0.002), and HT29 (59-fold decrease, p=0.008) In a full radiation clonogenic, the sensitizer enhancement ratio at 10% survival (SER10) was 3.8 (A549), 2.3 (U2OS), and 1.8 (HT29). In vitro metabolism studies have identified demethylated WNC0901 (WNC0901-M) as a major metabolite. Similar to the parent drug, WNC0901-M is a potent DNA-PK inhibitor with maximal radiosensitizing effects of the metabolite observed at 300nM with SER10 of 2.4 (A549), 1.9 (U2OS), and 1.8 (HT29). The in vivo efficacy of 50 mg/kg WNC0901 combined with 8 Gy was evaluated and compared to another DNA-PK inhibitor, 50 mg/kg peposertib, in A549 tumor heterotopic xenografts. Compared to placebo (median time to endpoint 63 days) or RT alone (median 72 days), the combination of WNC0901 and RT significantly delayed tumor regrowth (median 169 days; p=0.002 compared to RT). Interestingly, peposertib + RT was ineffective (median 83 days; p=0.332 compared to RT). In a second study, similarly robust sensitizing effects of WNC0901 was observed in HT29 xenografts (placebo - median 45 days; RT - median 75 days; RT + WNC0901 - median >110 days; p=.03 relative to RT). Together, these findings demonstrate the robust radiosensitizing effects of WNC0901 in radioresistant tumors and provide a rationale for continued development of this drug. Citation Format: Jessica L. Abraham, Ann C. Mladek, Sonia Jain, Shiv K. Gupta, Paul A. Decker, Matthew L. Kosel, Katrina K. Bakken, Brett L. Carlson, Lauren L. Ott, Danielle M. Burgenske, Jeanette E. Eckel-Passow, Wei Zhong, Jann N. Sarkaria. WNC0901 is a novel DNA-PK inhibitor and potent radiosensitizer of otherwise radioresistant solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6608.

Abstract Background: Melanoma brain metastases (MBM) represent a biologically distinct and clinically aggressive subset of melanoma. Comprehensive molecular characterization focused on intracranial disease remains limited. We aimed to define the genomic landscape of MBM and compare these findings with public melanoma datasets. Methods: We retrospectively identified 166 patients with histologically confirmed MBM across Mayo Clinic tri-sites (2011-2025). Molecular testing was performed in 123 patients (74.1%). Comprehensive next-generation sequencing (NGS; ≥300-gene platforms including Tempus, FoundationOne, Caris, NeoGenomics, and MayoComplete Solid Tumor Panel) was available in 41 patients (24.7%). Intermediate-depth profiling using the MayoComplete Melanoma Panel (17 genes) was performed in 21 patients (12.7%), and a targeted 5-gene melanoma driver panel (BRAF, NRAS, KIT, GNAQ, GNA11) in 52 patients (31.3%). cfDNA-based NGS was available in 12 patients (7.2%). Genomic alterations were summarized and benchmarked against TCGA-SKCM and MSK-IMPACT datasets via cBioPortal. We considered assay heterogeneity, as Mayo panels varied in sequencing depth and CNV coverage, and hybrid-capture platforms often under-detect TERT promoter hotspots. Results: Patients had a median age of 67 years, and 63% were male. BRAF mutations occurred in 48.8% (BRAF V600E/K 41.6%; non-V600 7.2%). Additional MAPK alterations included NRAS (17.5%), KIT (5.4%), GNAQ (1.2%), and GNA11 (0.6%). In the comprehensive NGS subset, tumor-suppressor alterations included CDKN2A (17.5%), PTEN (7.8%), NF1 (9.0%), TP53 (8.4%), and TERT promoter mutations (22.9%). Median TMB was 11.55 mut/Mb (IQR 5.0-25.5). Co-alterations included BRAF+CDKN2A (7.2%), BRAF+PTEN (4.2%), NRAS+CDKN2A (4.2%), NRAS+PTEN (1.2%), and CDKN2A+PTEN (3.6%); overall, 18.1% harbored MAPK driver + tumor-suppressor co-alterations. Compared with TCGA and MSK cohorts, MBM showed similar MAPK driver frequencies but fewer MAPK + tumor-suppressor co-alterations and lower CDKN2A, PTEN, and TP53 disruption, alongside a more prevalent of TERT promoter mutations (22.9% vs 7% and 2.1%), which may partly reflect coverage differences. Conclusion: MBM exhibit a distinct genomic profile characterized by reduced tumor-suppressor loss, fewer MAPK-tumor-suppressor co-alterations, and higher prevalence of TERT promoter mutations. While assay heterogeneity limits direct cross-cohort comparisons, these patterns suggest potentially distinct biological features of intracranial melanoma and underscore the need for harmonized, MBM-specific molecular studies to guide therapeutic development. Citation Format: Dina Elantably, Joseph B. Parker, Jakob Hamilton, Ahmed Abdelhakeem, Oluwatayo Adeoye, Saivaishnavi Kamatham, Winston Tan, Arkadiusz Z. Dudek, Anastasios Dimou, Matthew S. Block, Robert McWilliams, Svetomir Markovic, Mahesh Seetharam, Roxana Dronca, Ruqin Chen. Genomic landscape of melanoma brain metastases: Real-world analysis and comparison with public melanoma cohorts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2107.

Abstract Background: MHC class II molecules are normally restricted to professional antigen-presenting cells. Prior TNBC studies have focused on immune-compartment MHC-II. Using spatial transcriptomics, we recently identified aberrant tumor-cell MHC-II expression. Here, we characterized the immune architecture surrounding HLA-DRA-expressing tumor cells. Methods: High-plex single-cell spatial transcriptomics (CosMxTM SMI) was performed on treatment-naïve TNBC (Mayo TMA, n=65) and two neoadjuvant pembrolizumab cohorts (Mayo n=8; Emory n=4). Spatial neighborhoods were mapped relative to HLA-DRA-high tumor cells. Differential expression and adaptive immune gene-set scores were evaluated. Clinical relevance was assessed using FinXX (n=114), I-SPY2 (n=364), and Caris CODEai (n=3,662). Results: HLA-DRA-high tumor regions showed significant enrichment of adaptive immune subsets within ≤50 μm, including B cells, CD8+ T cells, NK cells, macrophages, and plasmacytoid dendritic cells. In pembrolizumab-treated cohorts, responders exhibited higher proportions of HLA-DRA-high tumor cells and greater numbers of B cells, plasmablasts, CD4+ and CD8+ T cells, and macrophages near tumor cells. Responders’ tumors showed upregulation of antigen-presentation machinery, B-cell/plasma-cell programs, CXCL13, and NOTCH3/DLL1. Across I-SPY2 and FinXX, higher HLA-DRA correlated with pathologic complete response and improved survival. In CODEai TNBC, high HLA-DRA expression correlated with higher CXCL13 (median TPM 7.5 vs. 1.1, q<0.05) and longer overall survival (24.2 vs. 18.5 months, HR 0.77, 95% CI 0.71-0.83, p<0.0001). This association was TNBC-specific. High CXCL13 also predicted improved survival (26.6 vs. 16.5 months, HR 0.64, 95% CI 0.59-0.69, p<0.0001). Conclusions: Tumor-cell HLA-DRA expression defines a highly organized adaptive immune niche enriched with B cells and activated T-cell populations in close proximity. Adaptive immune programs—including enhanced antigen presentation and CXCL13-mediated B-cell/plasma-cell pathways—consistently associate with improved outcomes across TNBC cohorts treated with chemotherapy and immune checkpoint blockade. Aberrant tumor-cell MHC-II expression may contribute to antitumor immunity and warrants further investigation as a potential therapeutic target. Citation Format: Yi Liu, Sachin Kumar Deshmukh, Thiti Susiriwatananont, Panuch Eiamprapaporn, Bogang Wu, Heikki Joensuu, Roberto A. Leon-Ferre, David Zahrieh, Judy C. Boughey, James Newell Ingle, Fergus J. Couch, Sharon Wu, Shipra Gandhi, Maryam Lustberg, George W. Sledge, Matthew P. Goetz, Keith L. Knutson, E Aubrey Thompson, Jodi Carter, Saranya Chumsri. Spatially resolved tumor-cell MHC class II shapes adaptive immunity and therapeutic response in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1200.

Abstract Breast cancer is the most common female malignancy in the U.S. and the leading cause of cancer-related deaths in women worldwide. Nearly 80% of breast tumors express estrogen receptor alpha (ERα) which drives tumor progression. While endocrine therapies and CDK4/6 inhibitors have increased overall survival, many patients experience recurrence of metastatic disease that is largely refractory to additional lines of endocrine therapy. Metastatic disease remains incurable, highlighting the need for alternative treatment approaches. Through a genome-wide CRISPR knockout screen, we identified Lemur Tail Kinase 2 (LMTK2) as the top hit exhibiting synthetic lethality in the presence of the endocrine therapy, endoxifen, in both endocrine-sensitive and -resistant cell line models. Using publicly available patient tumor databases we identified LMTK2 copy number gain/amplification in 67% of ERα+ human metastatic breast tumors, while only 1% of primary tumors harbored these genomic alterations. Elevated LMTK2 expression in early-stage primary breast tumors was associated with worse recurrence-free survival and overall survival. We discovered that LMTK2 mRNA and protein levels were substantially elevated in nearly all cell-line and patient-derived models of endocrine and CDK4/6i resistance. Utilizing siRNAs and dox-inducible shRNA expressing cell lines, we found that knockdown of LMTK2 enhanced the efficacy of multiple endocrine therapies in treatment naïve models and resensitized resistant models to their respective treatment. LMTK2 overexpression was found to be sufficient for conferring resistance to all standard of care therapies tested including endocrine agents, CDK4/6i, and PI3K inhibitors. RNAseq and global proteomics of LMTK2 overexpressing and knockout models revealed the PI3K/AKT pathway and estrogen signaling as highly regulated pathways by LMTK2. Confirmatory western blot studies demonstrated that LMTK2 overexpression results in dramatic increases in AKT and ERα phosphorylation and activity, while knockout of LMTK2 diminishes these effects. Attempts to completely ablate LMTK2 in endocrine/CDK4/6i-resistant models via CRISPR were not successful, and continuous siRNA or shRNA-mediated knockdown was lethal in resistant cell lines, confirming its essentiality in this setting. In summary we have uncovered LMTK2 as a novel mediator of endocrine/CDK4/6i resistance that is mechanistically linked to the PI3K/AKT pathway among others. Genetic manipulation of LMTK2 has validated it as an ideal therapeutic target for both advanced and newly diagnosed forms of ERα+ breast cancer. Citation Format: Anna Detry, Sofia Marigliano, Jenna Heinen, Adam Bass, Calley Jones, Rajeev Muthyala, Matthew Goetz, John Hawse. LMTK2: A novel driver of endocrine resistance in ERα+ breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 443.

Abstract Background: Primary Cardiac Lymphoma (PCL) is an exceptionally rare malignancy. While clinical case reports have postulated an association with human immunodeficiency virus (HIV), large-scale epidemiological data corroborating this link are limited. Objective: To determine whether HIV is associated with increased prevalence of PCL in a large, nationally representative sample of US hospitalizations. Methods: We performed a retrospective, cross-sectional analysis using the Healthcare Cost and Utilization Project National Inpatient Sample from 2016 to 2019, representing approximately 35 million unweighted hospitalizations. Hospitalizations were identified using a proxy definition for PCL, requiring ICD-10-CM codes for both cardiomyopathy (I42.*) and extranodal non-Hodgkin lymphoma (i.e., C83.39, C85.99). HIV status was identified using codes B20-B24 and Z21. We compared the prevalence of PCL in hospitalizations with versus without HIV and calculated odds ratios using Fisher’s Exact Test, reporting corresponding 95% confidence intervals (CI) and p-values. Results: We identified 569 hospitalizations meeting our operational definition for PCL out of 34,955,252 total discharges. The prevalence of PCL was significantly higher among hospitalizations with HIV (6.2 per 100,000) compared with those without HIV (1.6 per 100,000; p < 0.0001). HIV was associated with a 3.86-fold increased odds of PCL (OR: 3.86; 95% CI: 2.23-6.70; p < 0.0001). Conclusion: In this nationwide analysis of 35 million hospitalizations, HIV was strongly associated with a higher prevalence of PCL. Hospitalizations with HIV had nearly 4-fold higher odds of PCL, as defined by co-occurrence of cardiomyopathy and extranodal lymphoma, compared with hospitalizations without HIV. This population-level finding evinces the association previously suggested in clinical case reports. Citation Format: Omar Hozayen, Jay Hozayen, Benjamin Behers, Christoph Stephenson-Moe, Maha Hameed, Matthew Miller, Laura De Jesus Herrera, Anas Abu Jad, Rheiner Kammer, Mohab Idriss, Ahmed Aboutaleb, Bashar Roumia, Patricia Riano, Nicolas Riveros Neira, Brooke Hartenstein, Robert Taylor, Manuel Rosario Espinal, Brett M. Behers, Alya Hozayen, Mohamed Ibrahim, Karen Hamad. Primary cardiac lymphoma and HIV: A nationwide analysis of hospitalizations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 215.

Abstract Introduction. This study aimed to characterize the population pharmacokinetics (PopPK) of nab-paclitaxel following the administration of the nano-immunoconjugate nab-paclitaxel-bevacizumab to patients with unresectable stage IV melanoma or gynecological cancers (NCT02020707). The core objective was to characterize the population mean drug concentration-time profile, understand the variability in drug concentrations and identify factors that explain differences in paclitaxel plasma clearance. Also, we sought to explore the impact of the nano-immunoconjugate formulation on paclitaxel clearance. Methods. Using Pumas AI - an advanced pharmacokinetic software program, integrating Machine Learning methods - a PopPK analysis was conducted using concentration-time data collected from 29 subjects, (681 concentration measurements). Patients received 75 - 175 mg/m2 (total dose, 160 - 360 mg). Data were fitted to a 2-compartment model based biphasic decline observed in plasma concentration following intravenous administration. A critical requirement for the model was the inclusion of saturable elimination to accurately describe the drug’s disposition. A comprehensive modeling workflow was implemented, starting with base models (mixed vs. non-linear clearance), followed by the sequential addition of covariates (Age, Sex, BSA, Tumor, Cycle). The Parsimony Principle was applied to select the most appropriate model. Results. Population estimates for paclitaxel clearance and volume of distribution were 41.7 L/hr and 312 L, respectively. The analysis found paclitaxel clearance higher when compared with published data for nab-paclitaxel alone (21.1 or 25 L/h/m2; Sparreboom et al, CCR 11:4136, 2005 and Ando et al, CCP 69:457, 2012). A covariate model incorporating BSA and Gender is the best. For every 2 m2 increase in BSA, linear clearance (CL) increases by 6.9%. Females have 16.6% lower CL than males, which successfully characterized systematic differences in PK between subgroups of subjects. Conclusion. The administration of nab-paclitaxel via the nano-immunoconjugate formulation led to increased clearance for paclitaxel consistent with known clearance mechanisms for therapeutic antibodies. The differences in drug clearance among patients were primarily attributable to covariates BSA and gender. These findings are crucial for identifying safe and effective dosing regimens and informing future clinical simulations. Citation Format: Mohd Beshr Chama, Matthew Stephen Block, Vera J. Suman, Joel M. Reid. Population pharmacokinetics of nab-paclitaxel following administration of a nano-immunoconjugate with bevacizumab [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 301.